<p>In camouflaged object detection (COD), wholly and accurately segmenting the foreground from the background is a major focus of research. However, the similarity in color and texture between foreground targets and the background makes it difficult to distinguish them. Despite numerous deep learning networks utilizing different approaches for camouflaged object detection, precisely identifying object contours and enhancing the model’s resistance to interference from similar backgrounds remains a challenging problem. To address this issue, this paper proposes a camouflaged object detection network named boundary refinement network (BRNet) that achieves a fine-grained description of object contours by utilizing boundary semantic information constraints. Firstly, the multi-level asymmetric convolution module (MACM) is designed to enhance feature representation within the backbone architecture via a sequence of asymmetric convolutions and cross-layer connections. Additionally, the Boundary Constraint Guided Module (BCGM) is proposed to impose constraints on foreground shape and refine constrained foreground contours. Lastly, we introduce the Boundary Fusion Extraction Module (BFEM), which enables interaction between boundaries and objects in an additional dimension, leading to the generation of prediction results. Extensive quantitative and qualitative experiments conducted on three datasets demonstrate that BRNet performs well on the camouflaged object detection task, achieving superior results compared to 21 state-of-the-art approaches.</p>